Robot learning from few demonstrations by exploiting the structure and geometry of data (Talk)

Human-centric robotic applications often require the robots to learn new skills by interacting with the end-users. From a machine learning perspective, the challenge is to acquire skills from only few interactions, with strong generalization demands. It requires: 1) the development of intuitive active learning interfaces to acquire meaningful demonstrations; 2) the development of models that can exploit the structure and geometry of the acquired data in an efficient way; 3) the development of adaptive control techniques that can exploit the learned task variations and coordination patterns. The developed models often need to serve several purposes (recognition, prediction, online synthesis), and be compatible with different learning strategies (imitation, emulation, exploration). For the reproduction of skills, these models need to be enriched with force and impedance information to enable human-robot collaboration and to generate safe and natural movements. I will present an approach combining model predictive control and statistical learning of movement primitives in multiple coordinate systems. The proposed approach will be illustrated in various applications, with robots either close to us (robot for dressing assistance), part of us (prosthetic hand with EMG and tactile sensing), or far from us (teleoperation of bimanual robot in deep water).

Biography: Dr Sylvain Calinon is a permanent researcher at the Idiap Research
Institute (http://idiap.ch) since 2014, with research interests covering
robot learning and human-robot interaction. He is also a lecturer at the
Ecole Polytechnique Federale de Lausanne (EPFL), and an external
collaborator at the Department of Advanced Robotics (ADVR), Italian
Institute of Technology (IIT). From 2009 to 2014, he was a Team Leader
at ADVR, IIT. From 2007 to 2009, he was a Postdoc at the Learning
Algorithms and Systems Laboratory, EPFL. He holds a PhD from EPFL
(2007), awarded by the Robotdalen, ABB and EPFL-Press awards. He
co-authored about 90 publications in the field of robot learning, with
recognition including Best Paper Award at IEEE Ro-Man'2007 and Best
Paper Award Finalist at IEEE-RAS Humanoids'2009, IEEE/RSJ IROS'2013,
ICIRA'2015 and IEEE ICRA'2016. He currently serves as Associate Editor
in IEEE Robotics and Automation Letters, Springer Intelligent Service
Robotics, Frontiers in Robotics and AI, and the International Journal of
Advanced Robotic Systems.
Personal website: http://calinon.ch

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems